Primary Care Physicians Rate in 2023
The data source defines Primary Care Physicians Rate as the population of the county/city per physicians. This determinant of health analyzes people’s access to basic and everyday healthcare.
physicians_2023 <- ranked23_counties %>%
select(County,GEOID,`# Primary Care Physicians`)
names(physicians_2023)[3] = "Num_Primary_Care_Physicians"
physicians_plot <- plot_ly(data=physicians_2023, x= ~County, y= ~Num_Primary_Care_Physicians,
type= "bar") %>%
layout(barmode= "overlay",
title= " VA Primary Care Physicians 2023",
xaxis= list(title='County/City', tickangle= 40),
yaxis= list(title= "Number of Primary Care Physicians"))
physicians_plot
These are the top 10 counties/cities with the highest Primary Care Physician Rate.
## # A tibble: 10 × 3
## County GEOID Num_Primary_Care_Physicians
## <chr> <chr> <dbl>
## 1 Fairfax 51059 1271
## 2 Henrico 51087 350
## 3 Virginia Beach City 51810 334
## 4 Loudoun 51107 317
## 5 Chesterfield 51041 302
## 6 Richmond City 51760 246
## 7 Norfolk City 51710 244
## 8 Chesapeake City 51550 204
## 9 Prince William 51153 201
## 10 Arlington 51013 178
These are the top 10 counties/cities with the lowest Primary Care Physician Rate.
## # A tibble: 11 × 3
## County GEOID Num_Primary_Care_Physicians
## <chr> <chr> <dbl>
## 1 Amelia 51007 1
## 2 Appomattox 51011 1
## 3 Richmond 51159 1
## 4 Surry 51181 1
## 5 Buena Vista City 51530 1
## 6 Charles City 51036 2
## 7 Craig 51045 2
## 8 Essex 51057 2
## 9 King and Queen 51097 2
## 10 Lunenburg 51111 2
## 11 Williamsburg City 51830 2
library(stringr)
va.counties <- counties(state = 'Virginia')
## Retrieving data for the year 2021
physicians_2023$GEOID <- as.character(physicians_2023$GEOID)
va.physicians <- left_join(va.counties, physicians_2023, by = 'GEOID')
nColor <- 1271
colors <- paletteer_c(palette = 'viridis::mako', n = nColor, direction = -1)
plot(va.physicians$geometry, col = colors[va.physicians$Num_Primary_Care_Physicians], main = 'VCE Agents and Number of Primary Care Physicians')
points(agents_df$Long, agents_df$Lat, pch = 18, col = 'red', cex = 2)

Social Determinants with High Variance
We selected these social determinants because we thought these variables significantly affect people’s access to basic healthcare. We expect this list to grow and change as the summer progresses.